Yu Ke, Wang Yue, Shen Kaiquan, Li Xiaoping
Department of Mechanical Engineering, National University of Singapore, Singapore.
PLoS One. 2013 Oct 18;8(10):e76923. doi: 10.1371/journal.pone.0076923. eCollection 2013.
The common spatial pattern analysis (CSP), a frequently utilized feature extraction method in brain-computer-interface applications, is believed to be time-invariant and sensitive to noises, mainly due to an inherent shortcoming of purely relying on spatial filtering. Therefore, temporal/spectral filtering which can be very effective to counteract the unfavorable influence of noises is usually used as a supplement. This work integrates the CSP spatial filters with complex channel-specific finite impulse response (FIR) filters in a natural and intuitive manner. Each hybrid spatial-FIR filter is of high-order, data-driven and is unique to its corresponding channel. They are derived by introducing multiple time delays and regularization into conventional CSP. The general framework of the method follows that of CSP but performs better, as proven in single-trial classification tasks like event-related potential detection and motor imagery.
共同空间模式分析（CSP）是脑机接口应用中常用的特征提取方法，由于其纯粹依赖空间滤波的固有缺点，被认为是时不变的且对噪声敏感。因此，通常使用对抵消噪声不利影响非常有效的时间/频谱滤波作为补充。这项工作以自然直观的方式将CSP空间滤波器与复杂的特定通道有限脉冲响应（FIR）滤波器相结合。每个混合空间FIR滤波器都是高阶的、数据驱动的，并且与其相应通道是唯一对应的。它们是通过在传统CSP中引入多个时间延迟和正则化而推导出来的。该方法的总体框架遵循CSP的框架，但性能更好，如在事件相关电位检测和运动想象等单次试验分类任务中所证明的那样。
